A Multifault Diagnosis Method of Gear Box Running on Edge Equipment

Xiaoping Zhao ,Yonghong Zhang ,Shuiyong Fan

doi:10.1155/2020/8854236

Xiaoping Zhao , Yonghong Zhang + Show 1 more

Open Access

https://doi.org/10.1155/2020/8854236

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Abstract

In recent years, a large number of edge computing devices have been used to monitor the operating state of industrial equipment and perform fault diagnosis analysis. Therefore, the fault diagnosis algorithm in the edge computing device is particularly important. With the increase in the number of device detection points and the sampling frequency, mechanical health monitoring has entered the era of big data. Edge computing can process and analyze data in real time or faster, making data processing closer to the source, rather than the external data center or cloud, which can shorten the delay time. After using 8 bits and 16 bits to quantify the deep measurement learning model, there is no obvious loss of accuracy compared with the original floating-point model, which shows that the model can be deployed and reasoned on the edge device, while ensuring real time. Compared with using servers for deployment, using edge devices not only reduces costs but also makes deployment more flexible.

Highlights

Gearboxes play an important role in modern machinery and equipment, which are gradually developing toward complexity, precision, and intelligence
Experimental Verification. e data in Table 3 are input into the diagnostic model of convolution neural network + softmax classifier and the deep measurement learning model based on triplet loss, respectively, for model training
When using the data missing from a certain load to train the network and using this load data for network testing, the two models can obtain higher accuracy on the test set, but the deep measurement learning model is still better [24,25,26]

Summary

Introduction

Gearboxes play an important role in modern machinery and equipment, which are gradually developing toward complexity, precision, and intelligence. A gearbox is composed of gears, bearings, a shaft and box body, and other parts. It has the characteristics of a compact structure, high transmission efficiency, long service life, and reliable operation. It is an indispensable general component in modern industry, including aviation, power systems, automobiles, and industrial machine tools. Gears and bearings are two important parts of gearboxes, and they are prone to local faults due to fatigue, wear, and tear, leading to abnormal operation of gearboxes, which may cause economic losses, including damage to machines. The research of efficient gearbox condition monitoring and fault identification technology is of great significance for ensuring production safety, preventing and avoiding major accidents

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Conclusion